If the collected dataset \(D\)  is diverse enough to cover a large support of the distribution, the learned policy \(\pi (u/o; \theta )\)  could generalize to new unseen observations \(o_j\)  which are similar to the demonstrated observations, and predict a faithful control output \(u_j\) . Even so, distribution-mismatch is a common problem in LfD, where the demonstrated distribution does not match the test-time distribution, leading to unexpected compounding errors. A lot of methods have been suggested to overcome the distribution-mismatch problem [1]}, [2]}, [3]}. In this work, we employ the noise injection method suggested by [4]}, by inducing noise in the agent during demonstration (details of which are in Section REF ) to force it to visit novel states and improve the demonstration distribution.
